"""simple docstring"""



from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def 	_lowerCamelCase(	a = "laptop"				):
       __a       =    F"https://www.amazon.in/laptop/s?k={product}"
       __a       =    {
           "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
           "Accept-Language": "en-US, en;q=0.5",
       }
       __a       =    BeautifulSoup(requests.get(__lowerCAmelCase   ,    headers=__lowerCAmelCase				).text				)
       # Initialize a Pandas dataframe with the column titles
       __a       =    DataFrame(
           columns=[
               "Product Title",
               "Product Link",
               "Current Price of the product",
               "Product Rating",
               "MRP of the product",
               "Discount",
           ]				)
       # Loop through each entry and store them in the dataframe
       for item, _ in zip_longest(
           soup.find_all(
               "div"   ,    attrs={"class": "s-result-item", "data-component-type": "s-search-result"}   ,    )   ,    soup.find_all("div"   ,    attrs={"class": "a-row a-size-base a-color-base"}				)   ,    ):
              try:
                     __a       =    item.ha.text
                     __a       =    "https://www.amazon.in/" + item.ha.a["href"]
                     __a       =    item.find("span"   ,    attrs={"class": "a-offscreen"}				).text
                     try:
                            __a       =    item.find("span"   ,    attrs={"class": "a-icon-alt"}				).text
                     except AttributeError:
                            __a       =    "Not available"
                     try:
                            __a       =    (
                                "₹"
                                + item.find(
                                    "span"   ,    attrs={"class": "a-price a-text-price"}				).text.split("₹"				)[1]
                            )
                     except AttributeError:
                            __a       =    ""
                     try:
                            __a       =    float(
                                (
                                    (
                                        float(product_mrp.strip("₹"				).replace(","   ,    ""				)				)
                                        - float(product_price.strip("₹"				).replace(","   ,    ""				)				)
                                    )
                                    / float(product_mrp.strip("₹"				).replace(","   ,    ""				)				)
                                )
                                * 1_0_0				)
                     except ValueError:
                            __a       =    float("nan"				)
              except AttributeError:
                     pass
              __a       =    [
                  product_title,
                  product_link,
                  product_price,
                  product_rating,
                  product_mrp,
                  discount,
              ]
       __a       =    " "
       __a       =    " "
       data_frame.index += 1
       return data_frame


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]									=			"""headphones"""
   get_amazon_product_data(product).to_csv(F'''Amazon Product Data for {product}.csv''')

"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square(__lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												# BASE CASE
												if row >= rows or col >= cols:
																		return 0

												lowerCamelCase__   =update_area_of_max_square(__lowerCAmelCase  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						update_area_of_max_square(0  ,       0							)
						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square_using_dp_array(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												if row >= rows or col >= cols:
																		return 0
												if dp_array[row][col] != -1:
																		return dp_array[row][col]

												lowerCamelCase__   =update_area_of_max_square_using_dp_array(__lowerCAmelCase  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		lowerCamelCase__   =sub_problem_sol
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						lowerCamelCase__   =[[-1] * cols for _ in range(__lowerCAmelCase							)]
						update_area_of_max_square_using_dp_array(0  ,       0  ,       __lowerCAmelCase							)

						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[[0] * (cols + 1) for _ in range(rows + 1							)]
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =dp_array[row][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(dp_array[row][col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0

						return largest_square_area





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =current_row[col + 1]
																		lowerCamelCase__   =next_row[col + 1]
																		lowerCamelCase__   =next_row[col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(current_row[col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0
												lowerCamelCase__   =current_row

						return largest_square_area


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))


